Fatigue and alertness are related concepts—i.e. an increase in fatigue is typically associated with a decrease in alertness and vice-versa. Reduced levels of alertness and/or degraded performance associated with fatigue increases risk of being involved in accidents and/or reduced operational effectiveness that are often of concern in many industries, including without limitation transportation, health care, emergency response, manufacturing, mining, and space flight and in many activities, including without limitation participating in sports, driving, and/or the like. Factors that influence fatigue levels of individuals may include, without limitation, sleep history, time awake, time on task, work load, work schedule, light exposure, stimulant consumption, time of day, and/or the like. There is a general desire to predict the effects of fatigue on performance in terms contextually relevant performance metrics (e.g., risk of accident costing >$10,000) and, if possible, select appropriate countermeasures to improve such performance and reduce operational risk.
Mathematical models of fatigue allow computational approaches to predicting fatigue. Such models typically output a numerical score (a model-predicted fatigue level) that corresponds to an abstract representation of an individual's fatigue level. For example, a fatigue model may typically output a model-predicted fatigue level in the form of a number from zero (0) to one hundred (100). In some cases, such a model-predicted fatigue level may represent an individual's performance capacity as a percentage of some baseline (such as maximum capacity, normal capacity, or the like) determined when the individual is not affected by any fatigue-related factors. In some cases, the model-predicted fatigue level output by a mathematical fatigue model may be calibrated to a scale of another performance-related or fatigue-related test or physiological assay.
These types of model-predicted fatigue outputs do not easily translate into readily understandable performance metrics for real-world tasks. Without expert interpretation, it may be difficult to interpret the practical differences between an individual operating at a seventy-percent performance level versus a seventy-five-percent performance level.
There is a general desire to predict the effects of fatigue on performance in a manner that is relevant and easily understandable to those of non-expert skill in the field of fatigue science and fatigue management.